GCP Data Engineer

Key Responsibilities :

• Data Pipeline Development: Build and optimize ETL/ELT workflows using Airflow, Prefect, or similar orchestration tools.
• Data Integration & Transformation: Ingest data from multiple sources (transactional, behavioural, streaming) and prepare feature-ready datasets.
• Application Integration: Connect pipelines to Salesforce, Gainsight, and other enterprise applications for real-time data sync.
• BI Semantic Modelling: Design and maintain semantic layers for BI tools (Power BI, Looker, Tableau) to enable self-service analytics.
• Version Control & Experiment Tracking: Implement DVC for dataset versioning and integrate MLflow for experiment reproducibility.
• Distributed Data Processing: Use Spark, Ray, or Databricks for large-scale data transformations and feature engineering.
• Cloud Infrastructure: Deploy and manage pipelines on Azure Data Factory, Azure Synapse, GCP Dataflow, and Big Query leverage storage (ADLS, GCS) and compute clusters.
• MLOps Enablement: Collaborate on CI/CD workflows for ML models, feature store integration, and monitoring pipelines.
• Data Quality & Governance: Implement validation, lineage tracking, and compliance checks for secure and reliable data.
• Performance Optimization: Profile and tune pipelines for cost efficiency and low latency.
• Collaboration: Partner with Data Scientists to ensure timely delivery of clean, well-structured data for ML/DL models.

Required Skills : Technical:

• Python (pandas, PySpark), SQL familiarity with Scala is a plus.
• Orchestration: Airflow, Prefect, DBT.
• Distributed frameworks: Spark, Ray, Databricks.
• Cloud platforms: Azure (Data Factory, Synapse, ADLS) and GCP (Dataflow, BigQuery, GCS).
• BI Semantic Modelling: Power BI, Looker, Tableau.
• Application Integration: Salesforce, Gainsight, REST APIs.
• Containerization: Docker basic Kubernetes.
• Versioning: Git, DVC experiment tracking: MLflow.
• Streaming: Kafka or Pub/Sub for real-time ingestion.